Effective information extraction with semantic affinity patterns and relevant regions

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Publication Type Journal Article
School or College College of Engineering
Department Computing, School of
Creator Riloff, Ellen M.
Other Author Patwardhan, Siddharth
Title Effective information extraction with semantic affinity patterns and relevant regions
Date 2007
Description We present an information extraction system that decouples the tasks of finding relevant regions of text and applying extraction patterns. We create a self-trained relevant sentence classifier to identify relevant regions, and use a semantic affinity measure to automatically learn domain-relevant extraction patterns. We then distinguish primary patterns from secondary patterns and apply the patterns selectively in the relevant regions. The resulting IE system achieves good performance on the MUC-4 terrorism corpus and ProMed disease outbreak stories. This approach requires only a few seed extraction patterns and a collection of relevant and irrelevant documents for training.
Type Text
Publisher Association for Computational Linguistics
First Page 1
Last Page 11
Subject Information extraction; Semantic affinity patterns; Relevant regions; MUC-4 terrorism corpus; ProMed disease outbreak stories
Subject LCSH Information retrieval
Language eng
Bibliographic Citation Patwardhan, S., & Riloff, E. M. (2007). Effective information extraction with semantic affinity patterns and relevant regions. Proceedings of the 2007 Conference on Empirical Methods in Natural Language Processing (EMNLP-07), 1-11.
Rights Management (c)Patwardhan, S., & Riloff, E. M.
Format Medium application/pdf
Format Extent 108,829 bytes
Identifier ir-main,12405
ARK ark:/87278/s6hh735g
Setname ir_uspace
ID 702515
Reference URL https://collections.lib.utah.edu/ark:/87278/s6hh735g